4 research outputs found
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Chen et al. [Chen2022] recently published the article 'Fast and scalable
search of whole-slide images via self-supervised deep learning' in Nature
Biomedical Engineering. The authors call their method 'self-supervised image
search for histology', short SISH. We express our concerns that SISH is an
incremental modification of Yottixel, has used MinMax binarization but does not
cite the original works, and is based on a misnomer 'self-supervised image
search'. As well, we point to several other concerns regarding experiments and
comparisons performed by Chen et al
Rotation-Agnostic Image Representation Learning for Digital Pathology
This paper addresses complex challenges in histopathological image analysis
through three key contributions. Firstly, it introduces a fast patch selection
method, FPS, for whole-slide image (WSI) analysis, significantly reducing
computational cost while maintaining accuracy. Secondly, it presents PathDino,
a lightweight histopathology feature extractor with a minimal configuration of
five Transformer blocks and only 9 million parameters, markedly fewer than
alternatives. Thirdly, it introduces a rotation-agnostic representation
learning paradigm using self-supervised learning, effectively mitigating
overfitting. We also show that our compact model outperforms existing
state-of-the-art histopathology-specific vision transformers on 12 diverse
datasets, including both internal datasets spanning four sites (breast, liver,
skin, and colorectal) and seven public datasets (PANDA, CAMELYON16, BRACS,
DigestPath, Kather, PanNuke, and WSSS4LUAD). Notably, even with a training
dataset of 6 million histopathology patches from The Cancer Genome Atlas
(TCGA), our approach demonstrates an average 8.5% improvement in patch-level
majority vote performance. These contributions provide a robust framework for
enhancing image analysis in digital pathology, rigorously validated through
extensive evaluation. Project Page: https://rhazeslab.github.io/PathDino-Page/Comment: 23 pages, 10 figures, 18 tables. Histopathological Image Analysi
A Preliminary Investigation into Search and Matching for Tumour Discrimination in WHO Breast Taxonomy Using Deep Networks
Breast cancer is one of the most common cancers affecting women worldwide.
They include a group of malignant neoplasms with a variety of biological,
clinical, and histopathological characteristics. There are more than 35
different histological forms of breast lesions that can be classified and
diagnosed histologically according to cell morphology, growth, and architecture
patterns. Recently, deep learning, in the field of artificial intelligence, has
drawn a lot of attention for the computerized representation of medical images.
Searchable digital atlases can provide pathologists with patch matching tools
allowing them to search among evidently diagnosed and treated archival cases, a
technology that may be regarded as computational second opinion. In this study,
we indexed and analyzed the WHO breast taxonomy (Classification of Tumours 5th
Ed.) spanning 35 tumour types. We visualized all tumour types using deep
features extracted from a state-of-the-art deep learning model, pre-trained on
millions of diagnostic histopathology images from the TCGA repository.
Furthermore, we test the concept of a digital "atlas" as a reference for search
and matching with rare test cases. The patch similarity search within the WHO
breast taxonomy data reached over 88% accuracy when validating through
"majority vote" and more than 91% accuracy when validating using top-n tumour
types. These results show for the first time that complex relationships among
common and rare breast lesions can be investigated using an indexed digital
archive
Automatic Multi-Stain Registration of Whole Slide Images in Histopathology
Joint analysis of multiple biomarker images and tissue morphology is
important for disease diagnosis, treatment planning and drug development. It
requires cross-staining comparison among Whole Slide Images (WSIs) of
immuno-histochemical and hematoxylin and eosin (H&E) microscopic slides.
However, automatic, and fast cross-staining alignment of enormous gigapixel
WSIs at single-cell precision is challenging. In addition to morphological
deformations introduced during slide preparation, there are large variations in
cell appearance and tissue morphology across different staining. In this paper,
we propose a two-step automatic feature-based cross-staining WSI alignment to
assist localization of even tiny metastatic foci in the assessment of lymph
node. Image pairs were aligned allowing for translation, rotation, and scaling.
The registration was performed automatically by first detecting landmarks in
both images, using the scale-invariant image transform (SIFT), followed by the
fast sample consensus (FSC) protocol for finding point correspondences and
finally aligned the images. The Registration results were evaluated using both
visual and quantitative criteria using the Jaccard index. The average Jaccard
similarity index of the results produced by the proposed system is 0.942 when
compared with the manual registration.Comment: Accepted in EMBC 2021 : 43rd Annual International Conference of the
IEEE Engineering in Medicine and Biology Societ